A new application of X-ray scattering using principal component analysis – classification of vegetable oils

2005 ◽  
Vol 539 (1-2) ◽  
pp. 283-287 ◽  
Author(s):  
Gisele G. Bortoleto ◽  
Luiz Carlos M. Pataca ◽  
Maria Izabel M.S. Bueno
Keyword(s):  
Principal Component Analysis ◽  
Vegetable Oils ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

Applications of principal component analysis to pair distribution function data

2015 ◽  
Vol 48 (6) ◽  
pp. 1619-1626 ◽  
Author(s):  
Karena W. Chapman ◽  
Saul H. Lapidus ◽  
Peter J. Chupas
Keyword(s):  
Principal Component Analysis ◽  
Distribution Function ◽  
Pair Distribution Function ◽  
Principal Component ◽  
Component Analysis ◽  
Scattering Data ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

Developments in X-ray scattering instruments have led to unprecedented access toin situand parametric X-ray scattering data. Deriving scientific insights and understanding from these large volumes of data has become a rate-limiting step. While formerly a data-limited technique, pair distribution function (PDF) measurement capacity has expanded to the point that the method is rarely limited by access to quantitative data or material characteristics – analysis and interpretation of the data can be a more severe impediment. This paper shows that multivariate analyses offer a broadly applicable and efficient approach to help analyse series of PDF data from high-throughput andin situexperiments. Specifically, principal component analysis is used to separate features from atom–atom pairs that are correlated – changing concentration and/or distance in concert – allowing evaluation of how they vary with material composition, reaction state or environmental variable. Without requiring prior knowledge of the material structure, this can allow the PDF from constituents of a material to be isolated and its structure more readily identified and modelled; it allows one to evaluate reactions or transitions to quantify variations in species concentration and identify intermediate species; and it allows one to identify the length scale and mechanism relevant to structural transformations.

Principal Component Analysis of X-ray Diffraction Patterns To Yield Morphological Classification of Brucite Particles

2007 ◽  
Vol 79 (5) ◽  
pp. 2091-2095 ◽  
Author(s):  
Charlene R. S. Matos ◽  
Maria José Xavier ◽  
Ledjane S. Barreto ◽  
Nivan B. Costa ◽  
Iara F. Gimenez
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray Diffraction ◽  
Morphological Classification ◽  
X Ray ◽  
Diffraction Patterns

Classification of ab initio models of proteins restored from small-angle X-ray scattering

2018 ◽  
Vol 25 (5) ◽  
pp. 1379-1388 ◽  
Author(s):  
Mao Oide ◽  
Yuki Sekiguchi ◽  
Asahi Fukuda ◽  
Koji Okajima ◽  
Tomotaka Oroguchi ◽  
...  
Keyword(s):  
Ab Initio ◽  
Small Angle ◽  
Structural Information ◽  
Molecular Model ◽  
Dimensional Space ◽  
Principal Component ◽  
X Ray ◽  
X Ray Scattering ◽  
Ray Scattering

In structure analyses of proteins in solution by using small-angle X-ray scattering (SAXS), the molecular models are restored by using ab initio molecular modeling algorithms. There can be variation among restored models owing to the loss of phase information in the scattering profiles, averaging with regard to the orientation of proteins against the direction of the incident X-ray beam, and also conformational fluctuations. In many cases, a representative molecular model is obtained by averaging models restored in a number of ab initio calculations, which possibly provide nonrealistic models inconsistent with the biological and structural information about the target protein. Here, a protocol for classifying predicted models by multivariate analysis to select probable and realistic models is proposed. In the protocol, each structure model is represented as a point in a hyper-dimensional space describing the shape of the model. Principal component analysis followed by the clustering method is applied to visualize the distribution of the points in the hyper-dimensional space. Then, the classification provides an opportunity to exclude nonrealistic models. The feasibility of the protocol was examined through the application to the SAXS profiles of four proteins.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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